Journal of Infrastructure Systems

This paper introduces a constrained linear optimization model to help guide expenditure of government funds for regional hurricane risk management and to provide insight into some of the complexities involved in designing and prioritizing regional mitigation policies and programs. Specifically, it aims to help answer questions about (1) how much should be spent on mitigation (specifically, retrofitting or acquiring/demolishing buildings); (2) what the return on that investment will be; and (3) how mitigation funds should be spent (i.e., which buildings should be mitigated, how, and when). A full-scale case study for residential woodframe buildings in eastern North Carolina is presented to show how a model application can consider the important features of hurricane loss and mitigation while remaining computationally tractable for real, regional applications and to illustrate the type of results the model provides and how they can be interpreted. The case study considers damage from both high winds and storm surge flooding. The case study includes a detailed assessment of the risk by using a carefully selected set of hurricane scenarios to represent the regional hazard and a component-based damage model and considers physically realistic mitigation strategies.

Storm-related power outages cause approximately $270 million per year in repair costs in the United States. As a result of increasing sea surface temperatures caused by the changing climate, hurricane patterns (i.e., intensity/frequency) may change; however, there is much uncertainty as to how climate change may affect hurricane patterns. Implications of the changing hazard patterns on hurricane risk warrants an investigation to evaluate the potential impact of climate change on power distribution pole failure. This paper proposes a probabilistic framework to evaluate the vulnerability of power distribution poles to hurricanes under the potential impact of a changing climate. Two methods for the design of distribution poles in the United States, the National Electric Safety Code method and the ASCE method, are considered to investigate the difference of the vulnerability of a distribution pole subjected to hurricane hazard. The framework includes a reliability analysis of the designed power distribution poles using fragility analysis, the effects of degradation of timber poles, probabilistic wind models, and an assessment of the potential impacts of climate change on the annual failure probability of power distribution poles. This paper finds that climate change may have a significant effect on the structural failure probabilities of distribution poles. The age of the poles has a significant impact on the reliability of power distribution poles, which warrants the exploration of cost-effective methods to determine when a distribution pole should be replaced to ensure adequate strength to withstand wind loads.

Recent natural disasters have highlighted the need for increased planning for disruptive events. Forecasting damage and time that a system will be inoperable is important for disruption planning. The resilience of critical infrastructure systems, or their ability to recover quickly from a disruption, can mitigate adverse consequences of the disruption. This paper quantifies the resilience of a critical infrastructure sector through the dynamic inoperability input-output model (DIIM). The DIIM, which describes how inoperability propagates through a set of interdependent industry and infrastructure sectors following a disruptive event, includes a resilience parameter that has not yet been adequately assessed. This paper provides a data-driven approach to derive the resilience parameter through regression models. Data may contain different disruption scenarios, and regression models can incorporate these scenarios through the use of categorical or dummy variables. A mixed-effects model offers an alternate approach of accounting for these scenarios, and these models estimate parameters based on the combination of all scenarios (fixed effects) and an individual scenario (random effects). These regression models are illustrated with electric power outage data and a regional disruption that uses the DIIM to model production losses in Oklahoma following an electric power outage.

Transportation infrastructure could be vulnerable to local manifestations of global climate change, such as storm frequencies and durations of seasons. To adapt, transportation agencies need methodologies for reprioritizing their assets subject to the new sources of vulnerability. Prioritizing assets is nontrivial when criteria assessments and owner/operator preferences are considered in conjunction with the possible climate scenarios. Few efforts to date have addressed these scenarios in a priority setting for infrastructure asset management in the literature. This paper extends a scenario-based multicriteria decision framework that can assist decision makers in effectively allocating limited resources to adapt transportation assets to a changing climate. The framework is demonstrated with one of the most susceptible metropolitan transportation systems in the United States, the Hampton Roads region in coastal southeastern Virginia. First, the high-level goals of a long-range transportation plans are used in a traditional multicriteria analysis to generate a baseline prioritization of assets. Next, several scenarios that incorporate and combine a variety of climate conditions are identified. Finally, the scenarios are used to adjust the initial criteria weighting, which results in several reprioritizations of the assets. The results help to identify the most influential scenarios and characterize the sensitivity of the baseline prioritization across multiple scenarios. With these results, additional scientific and investigative efforts can be focused effectively to study and understand the influential scenarios.

Representing interdependent critical infrastructures is mandatory for implementing protection policies and strategies. Among the several interdependency models provided over the years, the input-output inoperability model (IIM) has attracted widespread attention due to its simplicity and compactness. Such a model can emphasize cascading effects induced in a complex scenario by dependencies and interdependencies; however, the model is typically set up based on economic data, and the stationary assumption greatly reduces the applicability of the framework. These aspects are crude approximations due to the intrinsic limits of the methodology. Indeed, in modeling realistic scenarios, the coupling of different infrastructures and sectors is expected to increase with outage duration. In this paper, a different formulation of the IIM is proposed where time-varying interdependency coefficients are considered. Such coefficients are defined to explicitly account for the severity and duration of negative phenomena. Some interesting results are obtained from a complex case study including several infrastructures in Italy, emphasizing the features of the proposed methodology. The proposed framework, based directly on operator experience, captures the behaviors induced by the various backup strategies.

Renegotiations are becoming an undesirable protagonist in infrastructure concessions, raising doubts about the merit of this procurement model. Renegotiations emerge as a consequence of contract incompleteness. When contracts are unable to forecast conditions in the long run and anticipate all possible contingencies, they become obsolete, and both parties must negotiate new terms. The economic and financial reequilibrium (EFR) model, applied in most infrastructure concessions contracts in Portugal, provides a tool to manage the renegotiation process. Given that it is not possible, or affordable, to write complete contracts because of high transaction costs, parties negotiate the rules under which the process of renegotiation might occur. By doing so, it is possible to reduce the incompleteness of contracts, but the model is not immune to opportunistic behaviors. This paper reflects on the effects of the EFR model by providing real data and a case study of a concession and provides some alternatives that are able to improve the performance and management of infrastructure contracts regarding the renegotiation phenomenon.

Transportation agencies around the world are using contractor-performed test results in construction and material acceptance and pay decisions. This practice is termed contractor acceptance testing (CAT) in this paper. Although CAT has helped transportation agencies to deal with a shrinking workforce and intensive construction schedules, it has been controversial. This paper provides insights into the technical and conceptual pitfalls of CAT. The technical pitfalls are revealed through Monte Carlo–based computer simulations of pavement specifications that use CAT. These simulations show that the verification process, which relies on the statistical F-test and t-test, is unreliable and can lead to erroneous pay decisions. The conceptual pitfalls are revealed by demonstrating that CAT encourages a quality-control approach that focuses on defect detection and containment, rather than defect prevention. Finally, potential alternatives and improvements to CAT are provided.

Mobile asset data collection vehicles can provide transportation agencies with inventories of various roadway and roadside elements containing location information, element type, and condition data. Horizontal curves are of interest to agencies because they have been shown to be hazardous roadway components and have potential for mobile data collection concurrently with other roadway elements. The cost of manually acquiring horizontal curvature data to develop an inventory can be prohibitive for many agencies, so understanding the applicability of a mobile asset data collection effort for curves is important. The objective of this work was to study the ability of multiple commercial roadway inventory vehicles and to compare them to other methods for determining the geometric characteristics of horizontal curves. The comparison is based on data from three commercial vendors of roadway asset inventory data on a 38.8-km (24.1-mi) course in central North Carolina. Among the 16 curves studied, at least one vendor was within 10% of the radius value found with the manual chord method for six of the curves and within 25% for 13 of the curves. Only three curves had a larger radius variant. For the length measurements of the 16 curves, at least one vendor was within 10% of the length from the chord method for eleven of the curves and within 25% for fifteen of the curves. Only one curve had a larger length variant. The mobile vehicle vendors provided more accurate and consistent curve length measurements than radius measurements. Agencies that consider using mobile data collection vehicles for horizontal alignments should understand the limitations of each horizontal curve estimating technique and the changes that can occur in the radius within the curve. Collaborating with vendors to define the changes in roadway alignment that constitute a curve, the associated beginning and ending of the curve, and geometric characteristics can provide agencies the most appropriate data to meet their needs.

Dynamic performance models that combine performance prediction and maintenance effectiveness are required for state-of-the-art optimization techniques such as optimal control. Because records of maintenance effectiveness depend on facility condition, nonlinear models are necessary to include interactions between variables to account for this dependence and estimate condition-dependent maintenance effectiveness. Therefore, this paper proposes a procedure for estimating nonlinear dynamic performance models that capture interactions between variables using panel data. The relationships between maintenance effectiveness and current facility condition and between structural design and traffic impact were found to be polynomial in a numerical example of highway pavements. It was also demonstrated that imposing physical constraints on the maintenance effectiveness based on external data sources generated more reasonable models when self-selected samples were used for estimation.

The authors have developed a new network-level pavement asset management system (PAMS) utilizing life-cycle analysis and optimization methods. Integrated life-cycle assessment and cost analysis expand the scope of the conventional network-level PAMS from raw material extraction to end-of-life management. To aid the decision-making process, the authors applied a life-cycle optimization model to determine the near-optimal preservation strategy for a pavement network. The authors utilized a geographic information system (GIS) model to enhance the network-level PAMS by collecting, managing, and visualizing pavement information data. The network-level pavement asset management system proposed in this paper allows decision makers to preserve a healthy pavement network and minimize life-cycle energy consumption, greenhouse gas (GHG) emissions, or cost as a single objective, and also meet budget constraints and other agency constraints within an analysis period. A case study of a pavement network in Michigan compares the near-optimal preservation strategy to the Michigan DOT’s current preservation practice. Compared with the current preservation plan, the optimal preservation strategy reduces life-cycle energy consumption, GHG emissions, and cost by 20, 24, and 10%, respectively. The authors also analyzed the impact of annual preservation budget cuts on total life-cycle cost. A US$3 million annual preservation budget reduction (75% reduction of current annual budget) will significantly increase user cost (caused by congestion and pavement surface deterioration) by US$450 million for a 40-year analysis period.

Ductile iron pipes have been used in North America since the late 1950s. This paper describes research that endeavored to gain a thorough understanding of the geometry of external corrosion pits and the factors (e.g., soil properties, appurtenances, and service connections) that influence this geometry. This understanding is subsequently used in the companion paper to devise a sampling scheme and to infer the condition of ductile iron buried water mains. Soil corrosivity is not a directly measurable parameter, and pipe external corrosion is primarily a random phenomenon. The literature is replete with methods and systems that attempt to use soil properties (e.g., resistivity, pH, and redox potential) to quantify soil corrosivity and subsequently predict pipe corrosion. In this research, varying lengths of ductile iron pipes were exhumed by several North American water utilities. The exhumed pipes were cut into short sections, sandblasted, and tagged. Soil samples were also obtained at discrete locations along the exhumed pipe. Pipe sections were scanned for external corrosion using a specially developed laser scanner. Scanned corrosion data were processed using specially developed software to obtain information on pit depth, pit area, and pit volume. Statistical analyses were subsequently performed on these three geometrical attributes. Various soil characteristics were investigated to determine their impact on the geometric properties of the corrosion pits. Subsequently, a method is proposed to assess the condition of a ductile iron pipe, based on the geometry of corrosion pits of several samples extracted along the pipe. This paper describes the pipe exhumation, data preparation, and statistical analysis of corrosion pits. The companion paper describes a sampling scheme to infer pipe condition of ductile iron buried water mains.

Ductile iron (DI) pipes have been used in North America since the late 1950s. This paper describes how understanding gained on the geometry of external corrosion pits is used to devise a sampling scheme and to infer the condition of ductile iron buried water mains. The companion paper describes the exhumation of varying lengths of ductile iron pipes in four North American water utilities. The exhumed pipes were cut into sections, sandblasted, and tagged. Soil samples extracted along the exhumed pipe were also provided. Pipe sections were scanned for external corrosion using a laser scanner to produce corrosion pit data sets. Statistical analyses were performed on geometric properties of corrosion pits such as pit depth, pit area, and pit volume. These analyses were developed further to assess the impact of the different soil characteristics on these pit properties. This paper describes the investigation of appropriate sampling schemes to represent the statistical properties of ductile iron pipe corrosion. With known statistical properties, an approach is developed to infer the condition of the pipe.